Anomalies detection in time-series data for the internal diagnostics of autonomous mobile robot
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388998%3A_____%2F20%3A00537806" target="_blank" >RIV/61388998:_____/20:00537806 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.21495/5896-3-508" target="_blank" >http://dx.doi.org/10.21495/5896-3-508</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21495/5896-3-508" target="_blank" >10.21495/5896-3-508</a>
Alternative languages
Result language
angličtina
Original language name
Anomalies detection in time-series data for the internal diagnostics of autonomous mobile robot
Original language description
Autonomous mobile robots are complex mechatronic machines which consists of numerous hardware and software modules working asynchronously to achieve desired behaviour. As there are many frameworks which helps to overcome the flat learning curve the problem of internal diagnostics arises. While users and developers are able to focus only on solving the high level problem algorithm or methods the internal states of the system is hidden. This helps to separate the users from solving hardware issues, which is helping until everything works properly. We present an algorithm which is able to detect anomalies in time based behaviour of the robot to improve the users confidence that the internal robot framework works correctly and as desired. The algorithm is based on probabilistic patterns detection based on Bayesian probabilistic theory.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ENGINEERING MECHANICS 2020
ISBN
978-80-214-5896-3
ISSN
1805-8248
e-ISSN
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Number of pages
4
Pages from-to
508-511
Publisher name
Brno University of Technology Institute of Solid Mechanics, Mechatronics and Biomechanics
Place of publication
Brno
Event location
Brno
Event date
Nov 24, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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